Shear-wave elasticity measurements of three-dimensional cell cultures for mechanobiology.

Studying mechanobiology in three-dimensional (3D) cell cultures better recapitulates cell behaviors in response to various types of mechanical stimuli in vivo Stiffening of the extracellular matrix resulting from cell remodeling potentiates many pathological conditions, including advanced cancers. However, an effective tool for measuring the spatiotemporal changes in elastic properties of such 3D cell cultures without directly contacting the samples has not been reported previously. We describe an ultrasonic shear-wave-based platform for quantitatively evaluating the spatiotemporal dynamics of the elasticity of a matrix remodeled by cells cultured in 3D environments. We used this approach to measure the elasticity changes of 3D matrices grown with highly invasive lung cancer cells and cardiac myoblasts, and to delineate the principal mechanism underlying the stiffening of matrices remodeled by these cells. The described approach can be a useful tool in fields investigating and manipulating the mechanotransduction of cells in 3D contexts, and also has potential as a drug-screening platform.

Identification of Neuronal Polarity by Node-Based Machine Learning.

Identifying the direction of signal flows in neural networks is important for understanding the intricate information dynamics of a living brain. Using a dataset of 213 projection neurons distributed in more than 15 neuropils of a Drosophila brain, we develop a powerful machine learning algorithm: node-based polarity identifier of neurons (NPIN). The proposed model is trained only by information specific to nodes, the branch points on the skeleton, and includes both Soma Features (which contain spatial information from a given node to a soma) and Local Features (which contain morphological information of a given node). After including the spatial correlations between nodal polarities, our NPIN provided extremely high accuracy (>96.0%) for the classification of neuronal polarity, even for complex neurons with more than two dendrite/axon clusters. Finally, we further apply NPIN to classify the neuronal polarity of neurons in other species (Blowfly and Moth), which have much less neuronal data available. Our results demonstrate the potential of NPIN as a powerful tool to identify the neuronal polarity of insects and to map out the signal flows in the brain's neural networks if more training data become available in the future.

Neuropeptide F inhibits dopamine neuron interference of long-term memory consolidation in Drosophila.

Long-term memory (LTM) formation requires consolidation processes to overcome interfering signals that erode memory formation. Olfactory memory in Drosophila involves convergent projection neuron (PN; odor) and dopaminergic neuron (DAN; reinforcement) input to the mushroom body (MB). How post-training DAN activity in the posterior lateral protocerebrum (PPL1) continues to regulate memory consolidation remains unknown. Here we address this question using targeted transgenes in behavior and electrophysiology experiments to show that (1) persistent post-training activity of PPL1-α2α'2 and PPL1-α3 DANs interferes with aversive LTM formation; (2) neuropeptide F (NPF) signaling blocks this interference in PPL1-α2α'2 and PPL1-α3 DANs after spaced training to enable LTM formation; and (3) training-induced NPF release and neurotransmission from two upstream dorsal-anterior-lateral (DAL2) neurons are required to form LTM. Thus, NPF signals from DAL2 neurons to specific PPL1 DANs disinhibit the memory circuit, ensuring that periodic events are remembered as consolidated LTM.